在风力轮机上测试蝙蝠死亡检测系统
Sara P Weaver1, Jon D Ritter2, Alexis M Commiskey1
1Natural Resources, Bowman Consulting, San Marcos, Texas, United States of America.
PloS one
|November 7, 2025
概括
研究人员开发了一种热摄像头系统和机器学习,以检测风力轮机中的蝙蝠死亡. 这项技术有助于识别潜在的蝙蝠死亡,指导搜索工作并改进保护策略.
科学领域:
- 野生动物保护 野生动物保护
- 可再生能源的影响.
- 机器学习应用 机器学习应用
背景情况:
- 风力轮机中的蝙蝠死亡是一个重要的保护问题.
- 目前的监测方法,比如热成像专注于旋转器扫描区域 (RSA),难以准确地检测死亡事故.
- 与广泛的视频录制工作相比,记录的蝙蝠死亡人数很少.
研究的目的:
- 评估一套新的地面热摄像系统与机器学习相结合,用于检测风力轮机中的蝙蝠死亡事故.
- 确定该系统在追踪和识别低于RSA的蝙蝠尸体中的有效性.
主要方法:
- 在德克萨斯州南部的风力轮机上部署了两个热摄像系统.
- 利用机器学习算法分析了来自视频录制的超过274,051条蝙蝠踪迹.
- 对算法识别的潜在死亡事件进行手动审查,并与地面搜索的蝙蝠尸体相关联.
主要成果:
- 机器学习算法从274,051个分类的蝙蝠轨迹中确定了189个潜在的蝙蝠死亡.
- 手动检查证实23条轨道可能是摄像机视野内发生的死亡事故.
- 确认的死亡人数与地面搜索的蝙蝠尸体和估计的死亡时间一致.
结论:
- 热摄像头系统和机器学习算法有效处理大量视频数据,以识别疑似蝙蝠死亡.
- 这个系统可以将人类搜索者引导到特定的轮机,最大限度地减少手动搜索的努力.
- 该技术有可能用于估计与海上风力轮机相关的蝙蝠死亡事故,而传统方法是不可行的.
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